Post-2020 Kunming 30% target can easily protect all endemic sharks and rays in the Western Indian Ocean and more
Bibliographic record
Abstract
Summary Sharks and rays are possibly the most threatened Class of marine fishes and their declines can be halted if protected areas are optimised to benefit these species. We identify spatial priorities for all 63 endemic sharks and rays in the marine biodiversity hotspot, the Western Indian Ocean (WIO). Collectively, while the WIO nations currently surpass the 10% Aichi ocean protection target, this amounts to a dismal protection of only 1.57% of each species’ distribution range. We show that the entire ranges of all endemics can be achieved by protecting 11% of EEZs of WIO nations, well within reach of the new 30% of oceans by 2030 target. Regional management bodies exist, which if taken advantage of to implement shark and ray management, provide opportunities to implement more efficient management across the region. We recommend key management actions to implement and explicit incentivisation of international cooperation in the post-2020 biodiversity framework. Science for Society The past decade has seen massive growth in the establishment of marine protected areas (MPAs), driven by the Aichi biodiversity target of protecting 10% of all ocean areas. This expansion of MPAs, however, has largely occurred in areas residual to extractive uses, often coinciding with less threatened areas of lower conservation value. This coming decade will see a further push to ensure 30% of the oceans are protected by 2030. It is important to understand how existing and future MPAs should be placed to benefit threatened biodiversity. Currently this is unclear for sharks and rays, comprising a species group that is the most evolutionarily distinct vertebrate radiation in the world and also one of the most threatened. We identify both regional and national conservation priorities for expanding marine protected areas to benefit all 63 endemic sharks and rays occurring in the Western Indian Ocean region. We find that the region has already exceeded the 10% ocean protection target, but this amounts to an average of only 1.57% protection of the distribution ranges of these species. We show that protecting the top 10% priority sites will conserve almost half of the geographic range of each species yet require only 1.16% of the total EEZ – a tiny fraction of the 30% by 2030 target. We also show that regional collaboration among all nations can result in more spatially efficient conservation priorities. We recommend that the post-2020 biodiversity framework needs to explicitly incentivise regional cooperation between nations to efficiently achieve urgent targets and maximise benefits to biodiversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".